Improving the Item Selection Process with Reinforcement Learning in Computerized Adaptive Testing

Yang Pian, Penghe Chen, Yu Lu, Guangchen Song, Pengtao Chen

Abstract: Item selection is the key process for computerized adaptive testing (CAT) to effectively assess examinees’ knowledge states. Existing item selection algorithms mainly rely on information metrics, suffering two issues: one is that the implicit cognitive information like relations between testing items as well as knowledge components cannot be captured by the information-based methods, and the other one is that the information-based algorithms computes item’s suitableness depending on examinees’ knowledge states which are estimated and imprecise inherently. To address these two issues, this work proposes to employ reinforcement learning technology to learn the item selection algorithm automatically in a data-driven manner. It is also able to properly capture the implicit cognitive relations between different testing items and avoid unnecessary item testing, and does not depend on examinees’ estimated knowledge states at all.